X-ray imaging has become a common tool for the inspection of agricultural commodities for defects, contaminants, and quality. Linescan x-ray units are replacing metal detectors in many processing plants because of their ability to detect non-metallic materials such as bones, glass, or rocks. While their use at present is mainly limited to the inspection of packaged food products, including canned foods and product packaged in jars, substantial research has been conducted in an effort to make real time x-ray inspection of fresh produce practical.
Schatzki et al. (1997) demonstrated the feasibility of detection of insect infestation and core rot in apples using a linescan x-ray system, although image quality was a major deterrent to detection of insects at the earlier life stages. Kim and Schatzki (2000) developed an algorithm that detected watercore damage in linescan x-ray images of apples. Tollner et al. (1992) used x-ray density as a measure of water content in apples and Talukter et al. (1998) developed algorithms for separation of agricultural commodities in x-ray images. In addition, x-ray inspection is a commonly used tool for quality control sampling of many agricultural products.
Despite this considerable research effort, real-time x-ray inspection of fresh produce is still uncommon in the industry, mainly because of limitations in image quality when using high-speed systems. Poor x-ray image quality is the main limiting factor for high-speed real time detection of many defects in fresh produce.
X-ray images of agricultural commodities often contain a deficiency that is a consequence of the round shape of the product being inspected. This is particularly true for the majority of fruit, as well as product packaged in jars and cans. Since it is necessary to apply enough x-ray energy to penetrate the thickest part of the sample, the thinner edges are often saturated and washed out in the x-ray image. The result is an image that is light in the center and becomes gradually darker towards the edges.
Alternatively, if the incident x-ray energy is reduced to allow imaging of the edges, there is often insufficient energy to penetrate the middle portion of the sample. While this is generally not a problem in identifying metal contaminants, which for the most part absorb all incident x-rays, less dense contaminants such as wood, bone, and even glass may not be detected because of this phenomenon if they are situated along the edges of the sample.
In order to compensate for the variation in pixel intensity across the image, it is common practice to normalize the image through a software correction. This is useful when automatic recognition algorithms are used to drive a rejection mechanism, as the algorithms can be affected by the lack of uniformity of image brightness. However, software corrections cannot recover information lost in the imaging process, such as the presence of a small object with low density situated along the edge of the sample that has been washed out due to saturation of the detectors.
A physical correction applied at the time of imaging would improve the overall image quality as well as increase the probability of detecting such contaminant.